Immune cell subsetting
Subset immune cells
allcells_css = readRDS(file = "data/processed/allcells_css.RDS")
Lymphoid cell analysis
Subset Lymphoid cells


DimPlot(all_imm_cells, reduction = "umap", group.by = "Condition")
DimPlot(all_imm_cells, reduction = "umap", group.by = "allcells_clusters")
DimPlot(all_imm_cells, reduction = "umap", group.by = "Donor")

DimPlot(all_imm_cells, reduction = "umap", group.by = "Phase")

FeaturePlot(all_imm_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
"CLEC1B", "CLEC14A", "PTPRC", "EPCAM",
"CD14", "TRAC", "CD3E", "CD8A"))

Cluster and get markers


all_l_cells = RunUMAP(all_l_cells, dims = 1:25, verbose = F)
DimPlot(all_l_cells, reduction = "umap", group.by = "Condition")
DimPlot(all_l_cells, reduction = "umap", group.by = "allcells_clusters")
DimPlot(all_l_cells, reduction = "umap", group.by = "Donor")

DimPlot(all_l_cells, reduction = "umap", group.by = "Phase")

FeaturePlot(all_l_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
"NKG7", "TRGC1", "PTPRC", "EPCAM",
"CD14", "TRAC", "CD3E", "CD8A"))

Add annotations


DimPlot(all_l_cells, reduction = "umap", group.by = "pca25_res.0.4", label = T)
DimPlot(all_l_cells, reduction = "umap", group.by = "Donor", label = F)
DimPlot(all_l_cells, reduction = "umap", group.by = "Condition", label = F)

Subset T/NK cells
new_l_labs = c("0" = "ab-T cells 1",
"1" = "NK/gd-T cells",
"2" = "ab-T cells 2",
"3" = "Infiltrating NK cells", # PTGDS,CX3CR1 (infilt)
"4" = "IgA+ Plasma cells",
"5" = "B cells",
"6" = "IgG+ Plasma cells",
"7" = "Dividing NK cells",
"8" = "ab-T cells (stress)")
all_l_cells$lymphoid_annot = new_l_labs[as.character(all_l_cells$pca25_res.0.4)]
Cluster and get markers
immune_pops = c("ab-T cells 1", "ab-T cells 2", "NK/gd-T cells", "Infiltrating NK cells")
all_t_cells = all_l_cells[,all_l_cells@meta.data$lymphoid_annot %in% immune_pops]
all_t_cells = suppressWarnings(SCTransform(all_t_cells, do.correct.umi = T, verbose = F,
vars.to.regress=c("unique_name","nCount_RNA"),
variable.features.rv.th = 1, seed.use = 1,
return.only.var.genes = F,
variable.features.n = NULL))
all_t_cells = RunPCA(all_t_cells, verbose = F)
all_t_cells = RunUMAP(all_t_cells, dims = 1:25, verbose = F)
DimPlot(all_t_cells, reduction = "umap", group.by = "Condition")

DimPlot(all_t_cells, reduction = "umap", group.by = "allcells_clusters")

DimPlot(all_t_cells, reduction = "umap", group.by = "lymphoid_annot")

DimPlot(all_t_cells, reduction = "umap", group.by = "Donor")

DimPlot(all_t_cells, reduction = "umap", group.by = "Phase")

FeaturePlot(all_t_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
"NKG7", "TRGC1", "PTPRC", "EPCAM",
"CD14", "TRAC", "CD3E", "CD8A"))

# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007630/
new_t_labs = c("0" = "NK cells 1",
"1" = "TRM cells", # may have CD4 and CD8; ITGA1
"2" = "MAIT cells 2", # CXCR6, CCR6, CCR5, some RORC (not DE with the ILC3), some ZBTB16
"3" = "NK cells 2",
"4" = "NK cells 3",
"5" = "CD8 ab-T cells 2",
"6" = "MAIT cells 1", # hard to be sure, but has many hallmarks, even higher CD8A
"7" = "Infiltrating NK cells",
"8" = "Naive CD4+ T cells",
"9" = "gd-T cells", # CD3+ vs cl7, TRDC/TRG
"10" = "CD8 ab-T cells 3",
"11" = "CD8 ab-T cells 1",
"12" = "Treg",
"13" = "ILC3") # KIT, AHR, RORC, no CD3/CD8/CD4
all_t_cells$t_annot = new_t_labs[as.character(all_t_cells$pca25_res.0.9)]
Myeloid cell analysis
Subset Myeloid cells
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007630/
new_t_labs = c("0" = "NK cells 1",
"1" = "TRM cells", # may have CD4 and CD8; ITGA1
"2" = "MAIT cells 2", # CXCR6, CCR6, CCR5, some RORC (not DE with the ILC3), some ZBTB16
"3" = "NK cells 2",
"4" = "NK cells 3",
"5" = "CD8 ab-T cells 2",
"6" = "MAIT cells 1", # hard to be sure, but has many hallmarks, even higher CD8A
"7" = "Infiltrating NK cells",
"8" = "Naive CD4+ T cells",
"9" = "gd-T cells", # CD3+ vs cl7, TRDC/TRG
"10" = "CD8 ab-T cells 3",
"11" = "CD8 ab-T cells 1",
"12" = "Treg",
"13" = "ILC3") # KIT, AHR, RORC, no CD3/CD8/CD4
Clustering and markers
immune_pops = c("cDCs 2", "Macrophages", "Kupffer cells", "cDCs 1", "pDCs")
all_m_cells = allcells_css[,allcells_css@meta.data$allcells_clusters %in% immune_pops]
all_m_cells = suppressWarnings(SCTransform(all_m_cells, do.correct.umi = T, verbose = F,
vars.to.regress=c("unique_name","nCount_RNA"),
variable.features.rv.th = 1, seed.use = 1,
return.only.var.genes = F,
variable.features.n = NULL))
all_m_cells = RunPCA(all_m_cells, verbose = F)
all_m_cells = RunUMAP(all_m_cells, dims = 1:25, verbose = F)
DimPlot(all_m_cells, reduction = "umap", group.by = "Condition")

DimPlot(all_m_cells, reduction = "umap", group.by = "allcells_clusters")

DimPlot(all_m_cells, reduction = "umap", group.by = "Donor")

DimPlot(all_m_cells, reduction = "umap", group.by = "Phase")

FeaturePlot(all_m_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
"NKG7", "TRGC1", "PTPRC", "EPCAM",
"CD14", "TRAC", "CD3E", "CD8A"))

Add annotations
mrks_q = SoupX::quickMarkers(all_m_cells@assays$SCT@counts,
all_m_cells@active.ident, N = 10)
View(mrks_q[mrks_q$qval<=0.05,])
new_m_labs = c("0" = "Kupffer cells",
"1" = "Monocytes/cDCs",
"2" = "Macrophages",
"3" = "Monocytes/cDCs",
"4" = "Kupffer cells",
"5" = "Monocytes/cDCs",
"6" = "Monocytes/cDCs",
"7" = "Macrophages",
"8" = "Monocytes/cDCs",
"9" = "cDC1",
"10" = "pDCs",
"11" = "pDCs",
"12" = "Dividing cDCs",
"13" = "Kupffer cells",
"14" = "Hepatocytes")
all_m_cells$mye_annot = new_m_labs[as.character(all_m_cells$pca25_res.0.6)]
Subset Monocytes
immune_pops = c("2", "7", "1", "6", "8", "3", "5", "4", "13", "0", "9")
all_mon_cells = all_m_cells[,all_m_cells@meta.data$pca25_res.0.6 %in% immune_pops &
all_m_cells@meta.data$allcells_clusters %in% c("Macrophages", "cDCs 1",
"cDCs 2", "Kupffer cells")]
all_mon_cells = suppressWarnings(SCTransform(all_mon_cells, do.correct.umi = T, verbose = F,
vars.to.regress=c("unique_name","nCount_RNA"),
variable.features.rv.th = 1, seed.use = 1,
return.only.var.genes = F,
variable.features.n = NULL))
all_mon_cells = RunPCA(all_mon_cells, verbose = F)
all_mon_cells = RunUMAP(all_mon_cells, dims = 1:25, verbose = F)
DimPlot(all_mon_cells, reduction = "umap", group.by = "Condition")
DimPlot(all_mon_cells, reduction = "umap", group.by = "allcells_clusters")
DimPlot(all_mon_cells, reduction = "umap", group.by = "pca25_res.0.6")
DimPlot(all_mon_cells, reduction = "umap", group.by = "Donor")
DimPlot(all_mon_cells, reduction = "umap", group.by = "Phase")
FeaturePlot(all_mon_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
"NKG7", "TRGC1", "PTPRC", "EPCAM",
"CD14", "TRAC", "CD3E", "CD8A"))
Clustering and markers
immune_pops = c("2", "7", "1", "6", "8", "3", "5", "4", "13", "0", "9")
all_mon_cells = all_m_cells[,all_m_cells@meta.data$pca25_res.0.6 %in% immune_pops &
all_m_cells@meta.data$allcells_clusters %in% c("Macrophages", "cDCs 1",
"cDCs 2", "Kupffer cells")]
all_mon_cells = suppressWarnings(SCTransform(all_mon_cells, do.correct.umi = T, verbose = F,
vars.to.regress=c("unique_name","nCount_RNA"),
variable.features.rv.th = 1, seed.use = 1,
return.only.var.genes = F,
variable.features.n = NULL))
all_mon_cells = RunPCA(all_mon_cells, verbose = F)
all_mon_cells = RunUMAP(all_mon_cells, dims = 1:25, verbose = F)
DimPlot(all_mon_cells, reduction = "umap", group.by = "Condition")

DimPlot(all_mon_cells, reduction = "umap", group.by = "allcells_clusters")

DimPlot(all_mon_cells, reduction = "umap", group.by = "pca25_res.0.6")

DimPlot(all_mon_cells, reduction = "umap", group.by = "Donor")

DimPlot(all_mon_cells, reduction = "umap", group.by = "Phase")

FeaturePlot(all_mon_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
"NKG7", "TRGC1", "PTPRC", "EPCAM",
"CD14", "TRAC", "CD3E", "CD8A"))

Add annotations


DimPlot(all_mon_cells, reduction = "umap", group.by = "pca25_res.0.5", label = T)
DimPlot(all_mon_cells, reduction = "umap", group.by = "allcells_clusters", label = T)
DimPlot(all_mon_cells, reduction = "umap", group.by = "Donor", label = F)

DimPlot(all_mon_cells, reduction = "umap", group.by = "Condition", label = F)

Put annotations on single immune cell object
Make dataframe with new annotations
new_m_labs = c("0" = "Kupffer cells",
"1" = "cDC2",
"2" = "Monocytes (IGSF21+ GPR34+)", # similar to those identified here https://www.nature.com/articles/s41586-020-2922-4
"3" = "Macrophages (HES4+)", # very similar to 5, HES4 is one of its most unique markers
"4" = "Kupffer cells (SUCNR1+)", # disproves https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1986575/; these KC are inflammatory/antiviral
"5" = "Macrophages",
"6" = "Monocytes (TREM2+ CD9+)", # in ramachandran et al, assoc with fibrotic scars
## in their projection, also close to KC
"7" = "cDC2",
"8" = "cDC1",
"9" = "Monocytes (secretory)", # related to 2 and 6
"10" = "activated DCs") # CD80, CD86, CCR7
all_mon_cells$mono_annot = new_m_labs[as.character(all_mon_cells$pca25_res.0.5)]
Add to the immune Seurat object
all_imm_cells = AddMetaData(all_imm_cells, metadata = newannot_df)
# the original "Dividing cells" will be relabeled "Dividing T/NK cells",
## and the newly annotated "Dividing NK cells" will be renamed to match this
all_imm_cells$immune_annot[is.na(all_imm_cells$immune_annot)] = "Dividing T/NK cells"
all_imm_cells$immune_annot[all_imm_cells$immune_annot=="Dividing NK cells"] = "Dividing T/NK cells"
all_imm_cells$immune_annot[all_imm_cells$immune_annot=="Hepatocytes"] = "Hepatocyte-Monocyte interaction"
DimPlot(all_l_cells, group.by = "lymphoid_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Lymphoid cells")
DimPlot(all_t_cells, group.by = "t_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("T and NK cells (subset of Lymphoid)")
DimPlot(all_m_cells, group.by = "mye_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Myeloid cells")
DimPlot(all_mon_cells, group.by = "mono_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Monocytes cells (subset of Myeloid)")
DimPlot(all_imm_cells, group.by = "allcells_clusters", reduction = "umap", label = T)+NoLegend()+ggtitle("Immune cells (original annotation)")
DimPlot(all_imm_cells, group.by = "immune_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Immune cells (new detailed annotation)")
Save Seurat objects
all_imm_cells = AddMetaData(all_imm_cells, metadata = newannot_df)
# the original "Dividing cells" will be relabeled "Dividing T/NK cells",
## and the newly annotated "Dividing NK cells" will be renamed to match this
all_imm_cells$immune_annot[is.na(all_imm_cells$immune_annot)] = "Dividing T/NK cells"
all_imm_cells$immune_annot[all_imm_cells$immune_annot=="Dividing NK cells"] = "Dividing T/NK cells"
all_imm_cells$immune_annot[all_imm_cells$immune_annot=="Hepatocytes"] = "Hepatocyte-Monocyte interaction"
DimPlot(all_l_cells, group.by = "lymphoid_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Lymphoid cells")

DimPlot(all_t_cells, group.by = "t_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("T and NK cells (subset of Lymphoid)")

DimPlot(all_m_cells, group.by = "mye_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Myeloid cells")

DimPlot(all_mon_cells, group.by = "mono_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Monocytes cells (subset of Myeloid)")

DimPlot(all_imm_cells, group.by = "allcells_clusters", reduction = "umap", label = T)+NoLegend()+ggtitle("Immune cells (original annotation)")

DimPlot(all_imm_cells, group.by = "immune_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Immune cells (new detailed annotation)")

---
title: "Immune population analysis"
output: html_notebook
---



# General Setup
Setup chunk

```{r, setup, include=FALSE}
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_knit$set(root.dir = normalizePath(".."))
knitr::opts_knit$get("root.dir")
```

Setup reticulate

```{r}
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
py_available(initialize = FALSE)
use_python(Sys.which("python"))
py_config()
```

Load libraries

```{r}
library(Seurat)
library(ggplot2)
library(destiny)
library(plyr)
library(dplyr)
library(RColorBrewer)
```

Load data (from all cells)

```{r}
allcells_css = readRDS(file = "data/processed/allcells_css.RDS")
```



# Immune cell subsetting
Subset immune cells

```{r}
# the dividing cells are T/NK, will update label at the end
immune_pops = c("ab-T cells", "gd-T cells", 
                "Plasmablasts", "cDCs 2", 
                "Macrophages", "Kupffer cells",
                "B cells",  "cDCs 1", "pDCs", "Dividing cells")
all_imm_cells = allcells_css[,allcells_css@meta.data$allcells_clusters %in% immune_pops]
all_imm_cells = suppressWarnings(SCTransform(all_imm_cells, do.correct.umi = T, verbose = F, 
                                             vars.to.regress=c("unique_name","nCount_RNA"),
                                             variable.features.rv.th = 1, seed.use = 1,
                                             return.only.var.genes = F, 
                                             variable.features.n = NULL))
all_imm_cells = RunPCA(all_imm_cells, verbose = F)
all_imm_cells = RunUMAP(all_imm_cells, dims = 1:25, verbose = F)
DimPlot(all_imm_cells, reduction = "umap", group.by = "Condition")
DimPlot(all_imm_cells, reduction = "umap", group.by = "allcells_clusters")
DimPlot(all_imm_cells, reduction = "umap", group.by = "Donor")
DimPlot(all_imm_cells, reduction = "umap", group.by = "Phase")
FeaturePlot(all_imm_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
                                                            "CLEC1B", "CLEC14A", "PTPRC", "EPCAM",
                                                            "CD14", "TRAC", "CD3E", "CD8A"))
```


## Lymphoid cell analysis
Subset Lymphoid cells

```{r}
immune_pops = c("ab-T cells", "gd-T cells", "Plasmablasts", "B cells")
all_l_cells = allcells_css[,allcells_css@meta.data$allcells_clusters %in% immune_pops]
all_l_cells = suppressWarnings(SCTransform(all_l_cells, do.correct.umi = T, verbose = F, 
                                             vars.to.regress=c("unique_name","nCount_RNA"),
                                             variable.features.rv.th = 1, seed.use = 1,
                                             return.only.var.genes = F, 
                                             variable.features.n = NULL))
all_l_cells = RunPCA(all_l_cells, verbose = F)
all_l_cells = RunUMAP(all_l_cells, dims = 1:25, verbose = F)
DimPlot(all_l_cells, reduction = "umap", group.by = "Condition")
DimPlot(all_l_cells, reduction = "umap", group.by = "allcells_clusters")
DimPlot(all_l_cells, reduction = "umap", group.by = "Donor")
DimPlot(all_l_cells, reduction = "umap", group.by = "Phase")
FeaturePlot(all_l_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
                                                            "NKG7", "TRGC1", "PTPRC", "EPCAM",
                                                            "CD14", "TRAC", "CD3E", "CD8A"))
```

Cluster and get markers

```{r}
all_l_cells = FindNeighbors(all_l_cells, reduction = "pca", dims = 1:25,
                            prune.SNN = 1/5, force.recalc = T, graph.name = "pca25")
all_l_cells = FindClusters(all_l_cells, algorithm = 2, verbose = F, graph.name = "pca25",
                           resolution = seq(0.1, 2, 0.1))
DimPlot(all_l_cells, reduction = "umap", group.by = "pca25_res.0.4", label = T)
DimPlot(all_l_cells, reduction = "umap", group.by = "Donor", label = F)
DimPlot(all_l_cells, reduction = "umap", group.by = "Condition", label = F)

all_l_cells = SetIdent(all_l_cells, value = "pca25_res.0.4")
mk_lcells = FindAllMarkers(all_l_cells, logfc.threshold = 0.2, pseudocount.use = 0.1)
write.csv(mk_lcells[mk_lcells$p_val_adj<=0.05,], 
          file = "results/immune/markers_lymphoid_subpop_all.csv", row.names = T, quote = F)


mk02 = FindMarkers(all_l_cells, ident.1 = "0", ident.2 = "2", 
                   logfc.threshold = 0.2, pseudocount.use = 0.1)
```

Add annotations

```{r}
new_l_labs = c("0" = "ab-T cells 1",
               "1" = "NK/gd-T cells",
               "2" = "ab-T cells 2",
               "3" = "Infiltrating NK cells", # PTGDS,CX3CR1 (infilt)
               "4" = "IgA+ Plasma cells",
               "5" = "B cells",
               "6" = "IgG+ Plasma cells",
               "7" = "Dividing NK cells",
               "8" = "ab-T cells (stress)")

all_l_cells$lymphoid_annot = new_l_labs[as.character(all_l_cells$pca25_res.0.4)]
```

Subset T/NK cells

```{r}
immune_pops = c("ab-T cells 1", "ab-T cells 2", "NK/gd-T cells", "Infiltrating NK cells")
all_t_cells = all_l_cells[,all_l_cells@meta.data$lymphoid_annot %in% immune_pops]
all_t_cells = suppressWarnings(SCTransform(all_t_cells, do.correct.umi = T, verbose = F, 
                                             vars.to.regress=c("unique_name","nCount_RNA"),
                                             variable.features.rv.th = 1, seed.use = 1,
                                             return.only.var.genes = F, 
                                             variable.features.n = NULL))
all_t_cells = RunPCA(all_t_cells, verbose = F)
all_t_cells = RunUMAP(all_t_cells, dims = 1:25, verbose = F)
DimPlot(all_t_cells, reduction = "umap", group.by = "Condition")
DimPlot(all_t_cells, reduction = "umap", group.by = "allcells_clusters")
DimPlot(all_t_cells, reduction = "umap", group.by = "lymphoid_annot")
DimPlot(all_t_cells, reduction = "umap", group.by = "Donor")
DimPlot(all_t_cells, reduction = "umap", group.by = "Phase")
FeaturePlot(all_t_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
                                                            "NKG7", "TRGC1", "PTPRC", "EPCAM",
                                                            "CD14", "TRAC", "CD3E", "CD8A"))
```

Cluster and get markers

```{r}
all_t_cells = FindNeighbors(all_t_cells, reduction = "pca", dims = 1:25,
                            prune.SNN = 1/5, force.recalc = T, graph.name = "pca25")
all_t_cells = FindClusters(all_t_cells, algorithm = 2, verbose = F, graph.name = "pca25",
                           resolution = seq(0.1, 2, 0.1))
DimPlot(all_t_cells, reduction = "umap", group.by = "pca25_res.0.9", label = T)
DimPlot(all_t_cells, reduction = "umap", group.by = "Donor", label = F)
DimPlot(all_t_cells, reduction = "umap", group.by = "Condition", label = F)
DimPlot(all_t_cells, reduction = "umap", group.by = "t_annot", label = T)

all_t_cells = SetIdent(all_t_cells, value = "pca25_res.0.9")
mk_tcells = FindAllMarkers(all_t_cells, logfc.threshold = 0.2, pseudocount.use = 0.1)
write.csv(mk_tcells[mk_tcells$p_val_adj<=0.05,], 
          file = "results/immune/markers_t_subpop_all.csv", row.names = T, quote = F)

saveRDS(mk_tcells, file = "./results/immune/clust_markers_t.RDS")

mk97 = FindMarkers(all_t_cells, ident.1 = "9", ident.2 = "7", 
                   logfc.threshold = 0.2, pseudocount.use = 0.1)
mk26 = FindMarkers(all_t_cells, ident.1 = "2", ident.2 = "6",
                   logfc.threshold = 0.2, pseudocount.use = 0.1)
mk813 = FindMarkers(all_t_cells, ident.1 = "8", ident.2 = "13",
                   logfc.threshold = 0.2, pseudocount.use = 0.1)
```



```{r}
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007630/
new_t_labs = c("0" = "NK cells 1",
               "1" = "TRM cells", # may have CD4 and CD8; ITGA1
               "2" = "MAIT cells 2", # CXCR6, CCR6, CCR5, some RORC (not DE with the ILC3), some ZBTB16
               "3" = "NK cells 2",
               "4" = "NK cells 3",
               "5" = "CD8 ab-T cells 2",
               "6" = "MAIT cells 1", # hard to be sure, but has many hallmarks, even higher CD8A
               "7" = "Infiltrating NK cells",
               "8" = "Naive CD4+ T cells", 
               "9" = "gd-T cells", # CD3+ vs cl7, TRDC/TRG
               "10" = "CD8 ab-T cells 3",
               "11" = "CD8 ab-T cells 1",
               "12" = "Treg",
               "13" = "ILC3") # KIT, AHR, RORC, no CD3/CD8/CD4

all_t_cells$t_annot = new_t_labs[as.character(all_t_cells$pca25_res.0.9)]
```


## Myeloid cell analysis
Subset Myeloid cells

```{r}
immune_pops = c("cDCs 2", "Macrophages", "Kupffer cells", "cDCs 1", "pDCs")
all_m_cells = allcells_css[,allcells_css@meta.data$allcells_clusters %in% immune_pops]
all_m_cells = suppressWarnings(SCTransform(all_m_cells, do.correct.umi = T, verbose = F, 
                                             vars.to.regress=c("unique_name","nCount_RNA"),
                                             variable.features.rv.th = 1, seed.use = 1,
                                             return.only.var.genes = F, 
                                             variable.features.n = NULL))
all_m_cells = RunPCA(all_m_cells, verbose = F)
all_m_cells = RunUMAP(all_m_cells, dims = 1:25, verbose = F)
DimPlot(all_m_cells, reduction = "umap", group.by = "Condition")
DimPlot(all_m_cells, reduction = "umap", group.by = "allcells_clusters")
DimPlot(all_m_cells, reduction = "umap", group.by = "Donor")
DimPlot(all_m_cells, reduction = "umap", group.by = "Phase")
FeaturePlot(all_m_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
                                                            "NKG7", "TRGC1", "PTPRC", "EPCAM",
                                                            "CD14", "TRAC", "CD3E", "CD8A"))
```

Clustering and markers

```{r}
all_m_cells = FindNeighbors(all_m_cells, reduction = "pca", dims = 1:25,
                            prune.SNN = 1/5, force.recalc = T, graph.name = "pca25")
all_m_cells = FindClusters(all_m_cells, algorithm = 2, verbose = F, graph.name = "pca25",
                           resolution = seq(0.1, 2, 0.1))
DimPlot(all_m_cells, reduction = "umap", group.by = "pca25_res.0.6", label = T)
DimPlot(all_m_cells, reduction = "umap", group.by = "Donor", label = F)
DimPlot(all_m_cells, reduction = "umap", group.by = "Condition", label = F)
DimPlot(all_m_cells, reduction = "umap", group.by = "allcells_clusters", label = F)

all_m_cells = SetIdent(all_m_cells, value = "pca25_res.0.6")
mk_mcells = FindAllMarkers(all_m_cells, logfc.threshold = 0.2, pseudocount.use = 0.1)
write.csv(mk_mcells[mk_mcells$p_val_adj<=0.05,], 
          file = "results/immune/markers_m_subpop_all.csv", row.names = T, quote = F)

saveRDS(mk_mcells, file = "./results/immune/clust_markers_myeloid.RDS")

mk1011 = FindMarkers(all_m_cells, ident.1 = "10", ident.2 = "11",
                     logfc.threshold = 0.2, pseudocount.use = 0.1)
```

Add annotations

```{r}
mrks_q = SoupX::quickMarkers(all_m_cells@assays$SCT@counts,
                             all_m_cells@active.ident, N = 10)
View(mrks_q[mrks_q$qval<=0.05,])

new_m_labs = c("0" = "Kupffer cells",
               "1" = "Monocytes/cDCs",
               "2" = "Macrophages",
               "3" = "Monocytes/cDCs",
               "4" = "Kupffer cells",
               "5" = "Monocytes/cDCs",
               "6" = "Monocytes/cDCs",
               "7" = "Macrophages",
               "8" = "Monocytes/cDCs",
               "9" = "cDC1",
               "10" = "pDCs",
               "11" = "pDCs",
               "12" = "Dividing cDCs",
               "13" = "Kupffer cells",
               "14" = "Hepatocytes")

all_m_cells$mye_annot = new_m_labs[as.character(all_m_cells$pca25_res.0.6)]
```

Subset Monocytes

```{r}
immune_pops = c("2", "7", "1", "6", "8", "3", "5", "4", "13", "0", "9")
all_mon_cells = all_m_cells[,all_m_cells@meta.data$pca25_res.0.6 %in% immune_pops &
                            all_m_cells@meta.data$allcells_clusters %in% c("Macrophages", "cDCs 1",
                                                                           "cDCs 2", "Kupffer cells")]
all_mon_cells = suppressWarnings(SCTransform(all_mon_cells, do.correct.umi = T, verbose = F, 
                                             vars.to.regress=c("unique_name","nCount_RNA"),
                                             variable.features.rv.th = 1, seed.use = 1,
                                             return.only.var.genes = F, 
                                             variable.features.n = NULL))
all_mon_cells = RunPCA(all_mon_cells, verbose = F)
all_mon_cells = RunUMAP(all_mon_cells, dims = 1:25, verbose = F)
DimPlot(all_mon_cells, reduction = "umap", group.by = "Condition")
DimPlot(all_mon_cells, reduction = "umap", group.by = "allcells_clusters")
DimPlot(all_mon_cells, reduction = "umap", group.by = "pca25_res.0.6")
DimPlot(all_mon_cells, reduction = "umap", group.by = "Donor")
DimPlot(all_mon_cells, reduction = "umap", group.by = "Phase")
FeaturePlot(all_mon_cells, reduction = "umap", features = c("MKI67", "ALB", "S100A8", "COLEC11",
                                                            "NKG7", "TRGC1", "PTPRC", "EPCAM",
                                                            "CD14", "TRAC", "CD3E", "CD8A"))
```

Clustering and markers

```{r}
all_mon_cells = FindNeighbors(all_mon_cells, reduction = "pca", dims = 1:25,
                            prune.SNN = 1/5, force.recalc = T, graph.name = "pca25")
all_mon_cells = FindClusters(all_mon_cells, algorithm = 2, verbose = F, graph.name = "pca25",
                           resolution = seq(0.1, 2, 0.1))
DimPlot(all_mon_cells, reduction = "umap", group.by = "pca25_res.0.5", label = T)
DimPlot(all_mon_cells, reduction = "umap", group.by = "allcells_clusters", label = T)
DimPlot(all_mon_cells, reduction = "umap", group.by = "Donor", label = F)
DimPlot(all_mon_cells, reduction = "umap", group.by = "Condition", label = F)

all_mon_cells = SetIdent(all_mon_cells, value = "pca25_res.0.5")
mk_mon = FindAllMarkers(all_mon_cells, logfc.threshold = 0.2, pseudocount.use = 0.1)
write.csv(mk_mon[mk_mon$p_val_adj<=0.05,], 
          file = "results/immune/markers_mon_subpop_all.csv", row.names = T, quote = F)

saveRDS(mk_mon, file = "./results/immune/clust_markers_mon.RDS")

mk61 = FindMarkers(all_mon_cells, ident.1 = "6", ident.2 = "1", 
                   logfc.threshold = 0.2, pseudocount.use = 0.1)
mk21 = FindMarkers(all_mon_cells, ident.1 = "2", ident.2 = "1", 
                   logfc.threshold = 0.2, pseudocount.use = 0.1)
```

Add annotations

```{r}
new_m_labs = c("0" = "Kupffer cells",
               "1" = "cDC2",
               "2" = "Monocytes (IGSF21+ GPR34+)", # similar to those identified here https://www.nature.com/articles/s41586-020-2922-4 
               "3" = "Macrophages (HES4+)", # very similar to 5, HES4 is one of its most unique markers
               "4" = "Kupffer cells (SUCNR1+)", # disproves https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1986575/; these KC are inflammatory/antiviral
               "5" = "Macrophages",
               "6" = "Monocytes (TREM2+ CD9+)", # in ramachandran et al, assoc with fibrotic scars
               ## in their projection, also close to KC
               "7" = "cDC2",
               "8" = "cDC1",
               "9" = "Monocytes (secretory)", # related to 2 and 6
               "10" = "activated DCs") # CD80, CD86, CCR7

all_mon_cells$mono_annot = new_m_labs[as.character(all_mon_cells$pca25_res.0.5)]
```


## Put annotations on single immune cell object
Make dataframe with new annotations

```{r}
newannot_l = list(ldf = all_l_cells@meta.data[,c("allcells_clusters", "lymphoid_annot")],
                  tdf = all_t_cells@meta.data[,c("allcells_clusters", "t_annot")],
                  mdf = all_m_cells@meta.data[,c("allcells_clusters", "mye_annot")],
                  mondf = all_mon_cells@meta.data[,c("allcells_clusters", "mono_annot")])
for(n in names(newannot_l)){
  newannot_l[[n]]$cells = rownames(newannot_l[[n]])
}

newannot_df = Reduce(function(x,y){merge(x,y, by = "cells", all = T)}, newannot_l)[,c(1,3,5,7,9)]

newannot_df$t_annot[is.na(newannot_df$t_annot)] = newannot_df$lymphoid_annot[is.na(newannot_df$t_annot)]
newannot_df$mono_annot[is.na(newannot_df$mono_annot)] = newannot_df$mye_annot[is.na(newannot_df$mono_annot)]
newannot_df$immune_annot = newannot_df$t_annot
newannot_df$immune_annot[is.na(newannot_df$immune_annot)] = newannot_df$mono_annot[is.na(newannot_df$immune_annot)]

newannot_df = data.frame(row.names = newannot_df$cells, 
                         immune_annot = newannot_df$immune_annot)
```

Add to the immune Seurat object

```{r, fig.width=12, fig.height=12}
all_imm_cells = AddMetaData(all_imm_cells, metadata = newannot_df)
# the original "Dividing cells" will be relabeled "Dividing T/NK cells",
## and the newly annotated "Dividing NK cells" will be renamed to match this
all_imm_cells$immune_annot[is.na(all_imm_cells$immune_annot)] = "Dividing T/NK cells"
all_imm_cells$immune_annot[all_imm_cells$immune_annot=="Dividing NK cells"] = "Dividing T/NK cells"
all_imm_cells$immune_annot[all_imm_cells$immune_annot=="Hepatocytes"] = "Hepatocyte-Monocyte interaction"

DimPlot(all_l_cells, group.by = "lymphoid_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Lymphoid cells")
DimPlot(all_t_cells, group.by = "t_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("T and NK cells (subset of Lymphoid)")
DimPlot(all_m_cells, group.by = "mye_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Myeloid cells")
DimPlot(all_mon_cells, group.by = "mono_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Monocytes cells (subset of Myeloid)")
DimPlot(all_imm_cells, group.by = "allcells_clusters", reduction = "umap", label = T)+NoLegend()+ggtitle("Immune cells (original annotation)")
DimPlot(all_imm_cells, group.by = "immune_annot", reduction = "umap", label = T)+NoLegend()+ggtitle("Immune cells (new detailed annotation)")
```

Save Seurat objects

```{r}
saveRDS(all_l_cells, file = "results/immune/all_l_cells.RDS")
saveRDS(all_t_cells, file = "results/immune/all_t_cells.RDS")
saveRDS(all_m_cells, file = "results/immune/all_m_cells.RDS")
saveRDS(all_mon_cells, file = "results/immune/all_mon_cells.RDS")
saveRDS(all_imm_cells, file = "results/immune/all_imm_cells.RDS")
```








